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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Psychoneuroendocrinology. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4788520
NIHMSID: NIHMS754852

Alcohol and tobacco consumption alter hypothalamic pituitary adrenal axis DNA methylation

Abstract

Alcohol and cigarette consumption have profound effects on genome wide DNA methylation and are common, often cryptic, comorbid features of many psychiatric disorders. This cryptic consumption is a possible impediment to understanding the biology of certain psychiatric disorders because if the effects of substance use are not taken into account, their presence may confound efforts to identify effects of other behavioral disorders. Since the hypothalamic pituitary adrenal (HPA) axis is known to be dysregulated in these disorders, we examined the potential for confounding effects of alcohol and cigarette consumption by examining their effects on peripheral DNA methylation at two key HPA axis genes, NR3C1 and FKBP5.

We found that the influence of alcohol and smoke exposure is more prominent at the FKBP5 gene than the NR3C1 gene. Furthermore, in both genes, loci that were consistently significantly associated with smoking and alcohol consumption demethylated with increasing exposure.

We conclude that epigenetic studies of complex disorders involving the HPA axis need to carefully control for the effects of substance use in order to minimize the possibility of type I and type II errors.

Keywords: DNA methylation, epigenetics, hypothalamic pituitary adrenal axis, psychiatric disorders, smoking, drinking

1. Introduction

One of the largest challenges to the development of an exact understanding of the molecular pathophysiology of individual psychiatric illnesses is that psychiatric disorders are frequently comorbid with one another. For example, according to the National Comorbidity Survey (NCS), subjects with major depression are 3 to 4 times more likely to also have alcohol dependence than those without depression (Kessler et al., 1997). In addition, those with depression are also more likely than most to experience other forms of substance use as well. This high co-morbidity of depression with substance use disorders is not unique. High rates of substance use disorders are found in most anxiety (e.g. panic disorder), psychotic disorders (e.g. schizophrenia) and other mood disorders (e.g. bipolar). Therefore, investigations seeking to isolate molecular signatures for processes associated with non-substance use disorders need to be concerned with the potential effects of co-morbid substance use among their subjects.

This is particularly true for alcohol and tobacco use disorders. Over the past several years, a number of studies have demonstrated the significant effects of cigarette consumption, and more recently alcohol consumption, on genome wide DNA methylation (Breitling et al., 2012; Dogan et al., 2014; Joubert et al., 2012; Monick et al., 2012; Philibert et al., 2014; Zeilinger et al., 2013). In particular, the genes whose methylation patterns are differentially affected by cigarette consumption preferentially map to gene networks implicated in stroke and heart disease (Dogan et al., 2014; Zhang et al., 2014). Furthermore, these and other studies have identified at least two smoke exposure associated epigenetic biomarkers (AHRR and F2RL3) with potential utility for the prevention and treatment of medical illness (Dogan et al., 2014; Philibert et al., 2015; Zhang et al., 2014).

Whether these effects of substance use also map to pathways relevant to the development of other psychiatric disorders is not as well understood. One particular process of interest that could be affected by substance use is the biological response to adversity. Adversity is associated with dysregulation of the hypothalamic pituitary adrenal (HPA) axis and is observed in those with psychiatric disorders including bipolar disorder and depression (Daban et al., 2005; Pariante and Lightman, 2008). Studies have suggested that stress alters DNA methylation at two key HPA axis genes: the glucocorticoid receptor (NR3C1) and its regulator, FK506 binding protein 5 (FKBP5) (Klengel et al., 2013; Non et al., 2012; Oberlander et al., 2008; Perroud et al., 2011).

While most investigators appreciate the need for adjusting the effects of substance use on adversity associated methylation changes, controlling for the exact extent of substance use in research subjects is difficult for at least two reasons. First, due to stigmatization and other adverse outcomes, self-report of smoking and drinking in high risk populations is often unreliable (Burgess et al., 2009; Caraballo et al., 2001; Erim et al., 2007; Russell et al., 2004; Whitford et al., 2009). Second, even if studies utilize biochemical verification of substance use status, current biological measures are known to have limited sensitivity (Florescu et al., 2009; Tavakoli et al., 2011). Hence, should the effects of cigarette or alcohol consumption influence the degree of DNA methylation at a locus of interest for these disorders, both type I and type II errors could arise.

This potential for confounding for genes in the HPA axis is not a theoretical issue. In our recent genome wide study of the effects of heavy alcohol consumption on DNA methylation, we identified a total of 8636 CpG residues whose methylation status were significantly associated with heavy alcohol intake (Philibert et al., 2014). With respect to the 1000 most significant CpG residues, 250 of these probes mapped to intergenic areas while 750 mapped to a total of 653 unique genes. Surprisingly, two genes had five genome wide significant associations mapping to their loci. The first was SLC1A5, a neutral amino acid transporter (Brauers et al., 2005). The second was FKBP5. When these recent results are taken together with our prior understanding of the co-morbidity of alcohol use disorder with psychiatric disorders, they suggest a need to better understand the potential for substance use to confound DNA methylation measurements at commonly studied candidate gene loci.

Therefore, in this communication, we take advantage of recently identified substance use methylation biomarkers and methylation data from three independent cohorts to examine the relationship of alcohol and cigarette consumption to DNA methylation at two key genes in the HPA axis, FKBP5 and NR3C1.

2. Materials and Methods

2.1 Informed consent

The protocols and procedures conducted in each study were approved by their respective Institutional Review Boards. The consent form, procedures, and protocols pertaining to the Family and Community Health Study (FACHS) study were approved by the Institutional Review Board at the University of Iowa, the University of Georgia and Iowa State University (Dogan et al., 2014). The Hannum study was approved by the Institutional Review Boards at the University of San Diego, the University of Southern California and West China Hospital (Hannum et al., 2013). The AlcMeth study was approved by the University of Iowa Institutional Review Board (Philibert et al., 2014).

2.2 Human subjects

The individuals included in this study were from the Family and Community Health Study (FACHS) cohort, an aging study (Hannum) and a study on methylation changes associated with alcohol consumption (AlcMeth). These cohorts have been described in previous studies (Dogan et al., 2014; Hannum et al., 2013; Philibert et al., 2014). The FACHS, Hannum and AlcMeth cohorts consisted of 180, 656 and 64 individuals, respectively. The demographics of these subjects are summarized in Table 1. On average, individuals in the Hannum cohort were over ten years older than those in the FACHS and AlcMeth cohorts.

Table 1
Demographic and methylation characteristics of subjects from the FACHS, Hannum and AlcMeth cohorts participating in the study

2.3 Genome-wide DNA methylation profiling

Peripheral blood mononuclear cell DNA methylation from the FACHS and AlcMeth cohorts and whole blood DNA methylation from the Hannum cohort was profiled using the Illumina (San Diego, CA) Infinium HumanMethylation450 BeadChip. The methylation data of all three cohorts are publically available and can be obtained from the Gene Expression Omnibus (GEO) database: GSE35059 and GSE59550 for FACHS, GSE40279 for Hannum and GSE57853 for AlcMeth. Beta values were derived using the Illumina Genome Studio software.

2.4 Analyses

For all analyses, the methylation at cg05575921 and cg23193759 were used as objective biomarkers to quantify smoking and alcohol consumption, respectively. Cg05575921 is located in intron 3 of the aryl hydrocarbon receptor repressor (AHRR) gene whereas cg23193759 is located on chromosome 10 open reading frame 35. The strong correlation between smoke exposure and methylation changes at cg05575921 is well established and has been consistently replicated (Philibert et al., 2015). While the relationship between alcohol consumption and cg23193759 methylation was only established recently, this locus has been shown to be the most differentially methylated with respect to alcohol use (Philibert et al., 2014). Both loci demethylate with increasing exposure.

There are 41 and 34 CpG sites contained within the Illumina 450K array for the NR3C1 and FKBP5 genes, respectively. Firstly, to determine the influence of smoking (represented by methylation at cg05575921) and alcohol (represented by methylation at cg23193759) on these genes, the average methylation at all loci within each gene was regressed against the biomarkers. Subsequently, to understand if the effects of alcohol and smoking consumption are concentrated at specific regions of the gene, a linear regression model was fitted for each of the 75 loci. Specifically, the methylation of the locus was regressed against each biomarker individually. From all fitted regression models, the regression coefficient, β, the coefficient of determination, R2, and the p-value were extracted. Correction for multiple comparisons was conducted by multiplying each p-value with 75. All analyses were performed in R (Team, 2012).

3. Results

The data for this study was derived from three independent cohorts (Table 1). The first cohort consisted of 180 individuals from the FACHS study (Dogan et al., 2014). The individuals from FACHS who contributed their data are African-American and mostly female (~62%) with an average age in their late 40s. The second cohort is from a study on the epigenetics of aging by Hannum and colleagues (Hannum et al., 2013). These individuals were either Northern European (~73%) or Hispanic (~27%), with an average age in the early 60s. The last cohort (referred to as AlcMeth), consisted of 64 individuals who participated in a commercial case and control study on the epigenetic effects of heavy alcohol consumption (Philibert et al., 2014). These individuals are almost all of Northern European ancestry, mostly male (75%) with their average age being in the mid-40s. Importantly, both the FACHS and the AlcMeth cohorts have high rates of substance use and comorbid medical disorders.

The average DNA methylation for the smoking biomarker, cg05575921, in the FACHS, Hannum and AlcMeth cohorts were 0.749, 0.821, and 0.814, respectively, while the average for the alcohol biomarker, cg23193759, was 0.171, 0.167, and 0.149, respectively (Table 1). As a reference, methylation in lifetime American non-smokers of Northern European ancestry at cg05575921 is approximately 0.91 while the methylation status at the cg23193759 locus in lifetime non-drinkers of Northern European ancestry is approximately 0.17 (Philibert et al., 2015; Philibert et al., 2014).

We examined the influence of cigarette smoking and alcohol consumption on the DNA methylation of the two frequently examined HPA axis genes, FKBP5 and NR3C1. These genes have 34 and 41 Illumina 450k array methylation probes mapping to them, respectively. Details on the placement of the probes, their mean and standard deviations in all three cohorts are provided in Appendix A. The influence of cigarette consumption, as indicated by demethylation at cg05575921, and alcohol consumption, as indicated by demethylation at cg23193759, in the FACHS, Hannum and AlcMeth cohorts were determined by fitting a linear regression model. The results of this analysis are summarized in Tables 2 and and3.3. The DNA methylation at FKBP5 was significantly associated with smoking and alcohol consumption in all three cohorts, with the most significant smoking and alcohol association observed in the FACHS (p<1.86E-06) and AlcMeth (p<2.16E-07) cohorts, respectively. Similarly, for DNA methylation at NR3C1, a significant association was only observed in the Hannum cohort with respect to alcohol consumption (p<0.0009). This implies that smoking and alcohol has a stronger influence on the DNA methylation of FKBP5 than NR3C1.

Table 2
Summary of the linear regression parameters in the FACHS, Hannum and AlcMeth cohorts at FKBP5
Table 3
Summary of the linear regression parameters in the FACHS, Hannum and AlcMeth cohorts at NR3C1

The distribution of substance use induced differential methylation at the generic structural level is still not well understood. To better understand this, we examined the effects of smoking and alcohol consumption at each locus in all three cohorts. The results from this analysis are summarized in Appendices B, ,CC and andDD for the FACHS, Hannum and AlcMeth cohorts, respectively. In all three cohorts, at the NR3C1 gene, only cg03857453 located in the body of the gene was significantly associated with smoking and drinking. The positive regression coefficient at this locus also suggests that, with increasing levels of smoke exposure and drinking (biomarkers hypomethylation), the methylation level at this locus decreases. While the number of significant associations was larger for FKBP5, the only significant CpG site for smoking common to all three cohorts was cg19226017, located at TSS1500. For alcohol consumption, the only two significant associations observed in all three cohorts were at cg03591753 located at the 5'UTR and cg14284211 located in the body of the gene. Once again, the positive regression coefficients at these loci imply that with increasing exposure, methylation level decreases.

4. Discussion

Examinations of the biology of human behavioral disorders are challenging to conduct for a number of reasons. One of those is the potential for unreliable self-report data (Caraballo et al., 2004; Corbett et al., 2012; Kandel et al., 2006; Webb et al., 2003). The strong effects of substance use on peripheral DNA methylation shown by ourselves and others suggest a potential for the effects of substance use to confound epigenetic analyses. This is particularly true when analyses are not corrected in any way for substance use.

In this study, we investigate the relationship between objective markers of cigarette and alcohol consumption and DNA methylation at two prominent HPA axis genes, FKBP5 and NR3C1, using DNA prepared from blood. The results demonstrate the stronger effects of these substances on the methylation status of the FKBP5 gene and the strong potential for substance use mediated confounding of DNA methylation analyses. The effects of alcohol consumption on FKBP5 methylation are not all that surprising. Prior studies have implicated FKBP5 sequence and expression variation in moderating responses to alcohol use. For example, a recent study by Huang and associates demonstrated that FKBP5 genetic variation moderated the severity of alcohol withdrawal in both humans and rodents (Huang et al., 2014). Using a genome wide approach, Bell and associates showed that acute alcohol intake in rats was associated with increased transcription of FKPB5 (Bell et al., 2009). Consequently, while current findings may have a chilling effect on some biomarker analyses of complex behavioral disorders, when taken together with prior genetic variation and gene expression studies, the current results actually suggest the additional need for further epigenetic and genetic examinations of the role of FKBP5 in moderating alcohol use disorders.

The need to control for substance use effects is probably not limited to studies of the HPA axis. Therefore, the potential for confounding may apply to virtually all epigenetic analyses of psychiatric candidate genes (de Leon and Diaz, 2005; Swendsen et al., 2010). Even so, simply using self-report of alcohol or smoking may not be sufficient to control for the effects of substance use. As compared to the gold standard of cotinine determinations, self-report of smoking is known to be unreliable in some high risk populations (Caraballo et al., 2004; Kandel et al., 2006; Russell et al., 2004). Substance use is not the only variable that needs to be considered as potential confounder in DNA methylation analyses. Age, gender, and body mass index are several variables that are known to have significant effects on genome wide DNA methylation signatures (Almén et al., 2014; Hannum et al., 2013). Fortunately, these variables are generally highly reliably assessed in most data sets. As such, their effects can be readily taken into account. In contrast, the presence or absence of other medical conditions such as type II diabetes, which also has significant genome wide effects (Toperoff et al., 2012), may not always be known, even by the individuals themselves. Hence, some degree of confounding will inevitably be present.

5. Conclusions

In summary, in this communication, we show the broad effects of alcohol and cigarette consumption on DNA methylation at the HPA axis with particularly prominent effects at FKBP5. These results highlight the need for controlling for the effects of substance use in epigenetic studies of complex disorders and the need for further studies on the role of the HPA axis in moderating alcohol use disorders.

Highlights

  • Objective DNA methylation biomarkers can quantify smoking and alcohol consumption.
  • Substance use affects HPA axis DNA methylation.
  • Influence of substance use is more prominent at FKBP5 than NR3C1.

Acknowledgements

The work in this manuscript was supported by National Institutes of Health grants R01DA037648 to Dr. Robert Philibert and R43AA022041 and R43DA037620 to Behavioral Diagnostics. Additional support for these studies was derived from the Center for Contextual Genetics and Prevention Science (Grant Number P30 DA027827, Dr. Gene Brody) funded by the National Institute on Drug Abuse. This research was supported in part through computational resources provided by The University of Iowa, Iowa City, Iowa. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Role of funding source This study was supported by National Institutes of Health and National Institute on Drug Abuse grants. Funding sources were not involved in the study design, in the collection, analysis and interpretation of data, in writing the report and the decision to submit the article for publication.

Appendices

Appendix A

The placement of NR3C1 and FKBP5 methylation probes with their respective means and standard deviations in the FACHS, Hannum and AlcMeth cohorts.

FACHSHannumAlcMeth
CpGGeneRegionIsland StatusMeanStandard DeviationMeanStandard DeviationMeanStandard Deviation
cg12466613NR3C1TSS15000.8950.0290.9250.0380.7460.036
cg07589972NR3C1TSS15000.8690.0270.9160.0310.8040.027
cg26720913NR3C11stExon0.0150.0070.0190.0120.0190.011
cg08818984NR3C11stExon0.0570.0170.0520.0210.0700.020
cg07528216NR3C15'UTRS_Shelf0.9180.0180.9440.0220.8650.019
cg27345592NR3C15'UTRS_Shore0.9230.0170.9400.0240.8510.021
cg13648501NR3C15'UTRS_Shore0.0730.0200.0640.0270.0970.025
cg24026230NR3C15'UTRS_Shore0.0250.0080.0160.0070.0240.009
cg14558428NR3C15'UTRIsland0.0240.0080.0160.0080.0180.008
cg21702128NR3C1TSS1500Island0.0700.0080.0950.0170.0690.010
cg10847032NR3C1TSS1500Island0.0420.0080.0530.0100.0480.012
cg16335926NR3C1TSS1500Island0.0170.0050.0130.0050.0130.005
cg18849621NR3C1TSS1500Island0.0550.0130.0540.0150.0590.018
cg06968181NR3C1TSS1500Island0.0630.0280.0260.0150.0280.014
cg26464411NR3C1TSS1500Island0.0670.0280.0270.0110.0340.010
cg18068240NR3C15'UTRIsland0.0120.0060.0050.0040.0100.005
cg15645634NR3C15'UTRIsland0.0280.0080.0170.0060.0250.007
cg15910486NR3C15'UTRIsland0.0900.0210.0460.0200.0550.010
cgO4111177NR3C15'UTRIsland0.0500.0060.0730.0150.0530.009
cg17860381NR3C15'UTRIsland0.0350.0110.0140.0080.0140.005
cg18019515NR3C1TSS200Island0.0110.0040.0060.0040.0080.004
cg11152298NR3C1TSS200Island0.0620.0060.0800.0110.0640.009
cg00629244NR3C1TSS200Island0.0140.0070.0070.0050.0160.007
cg18146873NR3C11stExonIsland0.0340.0100.0470.0190.0440.010
cg20753294NR3C11stExonIsland0.0620.0200.0390.0280.0550.018
cg17617527NR3C15'UTRIsland0.0080.0050.0050.0040.0070.004
cg06521673NR3C15'UTRIsland0.0400.0070.0430.0080.0440.008
cg06952416NR3C15'UTRN_Shore0.0680.0270.0410.0270.0740.032
cg27122725NR3C15'UTRN_Shore0.1090.0290.0880.0400.0600.028
cg18998365NR3C15'UTRN_Shore0.5510.0390.5800.0530.5330.040
cg07733851NR3C15'UTRN_Shore0.3200.0390.3540.0570.3440.037
cg08845721NR3C15'UTRN_Shore0.8270.0410.8910.0330.7940.031
cg17342132NR3C1BodyN_Shore0.7110.0600.8650.0300.7300.056
cg06613263NR3C1BodyN_Shelf0.7940.0440.8460.0410.7490.043
cg27107893NR3C1Body0.8600.0350.8970.0530.7430.053
cg25535999NR3C1Body0.8520.0240.8900.0270.8000.023
cg16586394NR3C1Body0.8660.0260.9010.0230.8370.021
cg18484679NR3C1Body0.8670.0240.9130.0340.7860.026
cg03857453NR3C1Body0.7540.0510.7000.0510.7570.049
cg19457823NR3C1Body0.7760.0730.8540.0540.7010.058
cg23273257NR3C13'UTR0.9340.0150.9560.0180.8870.017
cg08915438FKBP5TSS1500N_Shore0.5390.0500.5340.0570.5490.050
cg19226017FKBP5TSS1500N_Shore0.7660.0400.7190.0390.7220.034
cg25114611FKBP5TSS1500S_Shore0.3040.0340.3260.0380.2960.037
cg17030679FKBP55'UTRS_Shore0.0430.0090.0480.0120.0440.009
cg07485685FKBP55'UTRIsland0.0300.0090.0160.0100.0170.006
cg00610228FKBP55'UTRIsland0.0560.0070.0910.0240.0600.008
cg11845071FKBP55'UTRIsland0.0100.0060.0060.0050.0110.006
cg06937024FKBP55'UTRN_Shore0.0320.0150.0120.0060.0080.004
cg00052684FKBP55'UTRN_Shore0.3930.0620.4700.0560.3380.071
cg23416081FKBP55'UTRN_Shelf0.3130.0600.1880.0580.3340.080
cg15929276FKBP55'UTR0.1940.0530.1370.0520.1970.065
cg03591753FKBP55'UTRS_Shelf0.5470.0680.4720.0480.5870.065
cg08636224FKBP55'UTRS_Shore0.8960.0360.9080.0170.8760.016
cg00130530FKBP55'UTRS_Shore0.6120.0520.6310.0400.6070.048
cg20813374FKBP55'UTRS_Shore0.4110.0490.4040.0400.3990.046
cg01294490FKBP5TSS200S_Shore0.0890.0170.0800.0170.1000.024
cg07843056FKBP5TSS200Island0.0080.0060.0070.0060.0120.008
cg16012111FKBP5TSS200Island0.0580.0100.0750.0170.0630.011
cg10913456FKBP51stExonIsland0.0070.0040.0070.0110.0070.004
cg00140191FKBP55'UTRIsland0.0450.0140.0250.0130.0280.010
cg00862770FKBP55'UTRIsland0.0380.0070.0370.0080.0450.011
cg03546163FKBP55'UTRN_Shore0.5030.0740.5500.0970.4230.062
cg14642437FKBP55'UTRN_Shelf0.8440.0270.8420.0350.7970.026
cg17085721FKBP55'UTR0.8560.0250.9070.0200.8440.025
cg19014730FKBP55'UTR0.6510.0500.7260.0550.5790.050
cg07061368FKBP55'UTR0.8130.0450.8800.0420.7290.045
cg08586216FKBP55'UTR0.9160.0120.9380.0140.9030.014
cg16052510FKBP5Body0.7290.0590.8470.0490.7090.049
cg14284211FKBP5Body0.2460.0500.1510.0470.2250.054
cg07633853FKBP5Body0.1590.0370.1090.0500.1850.044
cg10300814FKBP5Body0.8810.0190.8900.0200.8600.016
cg06087101FKBP5Body0.4120.0810.4120.0730.4070.064
cg02665568FKBP5Body0.8650.0270.8910.0320.8000.025

Appendix B

The smoking and alcohol consumption associated regression coefficient, percent variation explained and corrected p-value of DNA methylation probes at the NR3C1 and FKBP5 genes in the FACHS cohort.

SmokingAlcohol Consumption
CpGGeneRegression Coefficient% Variation ExplainedCorrected P-valueRegression Coefficient% Variation ExplainedCorrected P-value
cg12466613KR3C1−0.02930.891−0.18493.311
cg07589972KR3C10.05163.181−0.1211.631
cg26720913KR3C10.0091.3810.05284.550.355
cg08818984KR3C10.01270.510.08432.011
cg07528216KR3C10.0231.4410.06921.211
cg27345592KR3C10.0007010.08191.871
cg13648501KR3C1−0.02741.761−0.10922.591
cg24026230KR3C1−0.00190.061−0.01920.541
cg14558428KR3C1−0.01894.720.253−0.02750.931
cg21702128KR3C10.00280.1210.05153.830.651
cg10847032KR3C10.01453.2510.04673.121
cg16335926KR3C1−0.00170.111−0.01290.611
cg18849621KR3C10.02453.171−0.01910.181
cg06968181KR3C10.03121.1410.09761.051
cg26464411KR3C1−0.0021010.03870.161
cg18068240KR3C1−0.00320.261−0.00310.021
cg15645634KR3C1−0.01623.680.742−0.02640.911
cg15910486KR3C10.02040.8710.05170.521
cg04111177KR3C10.00841.6710.03092.11
cg17860381KR3C1−0.0010.011−0.001401
cg18019515NR3C10.00792.8310.01811.381
cg11152298NR3C10.00410.3710.0342.341
cg00629244NR3C1−0.0004010.01240.311
cg18146873NR3C1−0.00730.521−0.01390.171
cg20753294NR3C1−0.00480.0510.08111.381
cg17617527NR3C10.00571.3210.000801
cg06521673NR3C1−0.0020.0710.00570.051
cg06952416NR3C10.05774.310.418−0.06990.581
cg27122725NR3C1−0.0351.281−0.11451.311
cg18998365NR3C10.08264.030.5170.16911.571
cg07733851NR3C10.10136.180.0570.16791.581
cg08845721NR3C10.08043.410.9810.06150.191
cg17342132NR3C10.00960.0210.11490.311
cg06613263NR3C10.11546.140.0600.000101
cg27107893NR3C10.05612.261−0.16071.731
cg25535999NR3C10.03752.261−0.04390.291
cg16586394NR3C10.05063.31−0.24737.340.018
cg18484679NR3C10.0473.560.839−0.12552.361
cg03857453NR3C10.16659.450.0020.660213.842.04E-05
cg19457823NR3C10.06080.631−0.72088.290.007
cg23273257NR3C10.01891.4710.07642.251
cg08915438FKBP50.1013.740.6940.18531.171
cg19226017FKBP50.11637.470.0150.13480.941
cg25114611FKBP50.10889.060.0030.15541.721
cg17030679FKBP50.00870.8810.01940.41
cg07485685FKBP50.01241.7810.01320.191
cg00610228FKBP50.01473.610.7950.03351.751
cg11845071FKBP50.00230.151−0.00550.081
cg06937024FKBP50.00780.2510.01290.061
cg00052684FKBP50.0420.4210.22891.151
cg23416081FKBP50.12624.040.5130.51546.270.052
cg15929276FKBP50.04640.6910.030.031
cg03591753FKBP50.16095.130.1670.887914.559.50E-06
cg08636224FKBP50.02810.5610.06260.261
cg00130530FKBP50.16649.330.002−0.17290.941
cg20813374FKBP50.193414.031.66E-050.03650.051
cg01294490FKBP50.02561.9810.05330.81
cg07843056FKBP50.012.331−0.02280.831
cg16012111FKBP50.0005010.0280.671
cg10913456FKBP5−0.00622.1510.0020.021
cg00140191FKBP50.01641.2610.03390.511
cg00862770FKBP5−0.0070.8110.01110.191
cg03546163FKBP50.10871.9410.52024.130.465
cg14642437FKBP50.05894.190.4380.324211.831.70E-04
cg17085721FKBP50.02080.6210.16133.540.895
cg19014730FKBP50.06081.341−0.21191.511
cg07061368FKBP5−0.00930.041−0.19631.581
cg08586216FKBP50.02574.080.490.09955.680.096
cg16052510FKBP50.01930.110.18720.861
cg14284211FKBP50.11064.410.3490.915728.141.40E-12
cg07633853FKBP50.03710.8910.31576.020.07
cg10300814FKBP50.0485.670.0960.15655.610.102
cg06087101FKBP50.10631.5510.12010.181
cg02665568FKBP5−0.0018010.10541.261
cg18726036FKBP5−0.0162.791−0.01210.151

Appendix C

The smoking and alcohol consumption associated regression coefficient, percent variation explained and corrected p-value of DNA methylation probes at the NR3C1 and FKBP5 genes in the Hannum cohort.

SmokingAlcohol Consumption
CpGGeneRegression Coefficient% Variation ExplainedCorrected P-valueRegression Coefficient% Variation ExplainedCorrected P-value
cg12466613NR3C1−0.01850.131−0.03150.081
cg07589972NR3C1−0.00910.051−0.03250.131
cg26720913NR3C1−0.00410.0710.03410.931
cg08818984NR3C10.01510.2710.11413.242.87E-04
cg07528216NR3C10.00790.0710.00820.011
cg27345592NR3C1−0.00410.0110.05460.561
cg13648501NR3C10.0110.0810.1051.640.078
cg24026230NR3C1−0.000201−0.00860.171
cg14558428NR3C1−0.00920.7610.02691.320.285
cg21702128NR3C10.01650.510.05631.250.309
cg10847032NR3C1−0.00930.431−0.01870.371
cg16335926NR3C1−0.00150.0510.000101
cg18849621NR3C1−0.0030.021−0.01530.121
cg06968181NR3C1−0.00940.191−0.03010.341
cg26464411NR3C1−0.00640.1710.001501
cg18068240NR3C1−0.00170.111−0.0010.011
cg15645634NR3C1−0.00340.1610.00260.021
cg15910486NR3C1−0.0311.260.2940.02380.161
cg04111177NR3C10.0013010.04250.891
cg17860381NR3C1−0.00120.011−0.0020.011
cg18019515NR3C1−0.00110.041−0.00310.061
cg11152298NR3C1−0.01180.610.01630.241
cg00629244NR3C1001−0.00710.231
cg18146873NR3C10.0170.4210.05420.921
cg20753294NR3C1−0.00590.021−0.01170.021
cg17617527NR3C10.00130.0610.00420.151
cg06521673NR3C10.00130.0110.01210.261
cg06952416NR3C10.00440.0110.004501
cg27122725NR3C1−0.001901−0.01380.011
cg18998365NR3C10.06360.7510.26152.730.002
cg07733851NR3C10.09561.460.1440.15620.841
cg08845721NR3C10.02120.211−0.08470.741
cg17342132NR3C1−0.01290.091−0.04630.261
cg06613263NR3C10.00401−0.14361.340.222
cg27107893NR3C10.06030.6710.06670.181
cg25535999NR3C10.02590.491−0.03730.221
cg16586394NR3C10.01130.121−0.03550.261
cg18484679NR3C10.0013010.01220.011
cg03857453NR3C10.14384.179.93E-060.43858.353.35E-12
cg19457823NR3C1−0.00950.021−0.22181.90.030
cg23273257NR3C1−0.00960.1510.04380.661
cg08915438FKBP50.14743.549.27E-050.23381.920.028
cg19226017FKBP50.08092.250.0090.1271.20.377
cg25114611FKBP50.05321.010.7460.21863.675.69E-05
cg17030679FKBP50.00150.0110.0180.251
cg07485685FKBP5−0.01160.711−0.00430.021
cg00610228FKBP5−0.00710.041−0.01130.021
cg11845071FKBP50.00370.3210.00430.091
cg06937024FKBP5−0.00390.191−0.00710.141
cg00052684FKBP50.06460.71−0.07780.221
cg23416081FKBP50.09841.490.1310.63213.24.98E-20
cg15929276FKBP50.03130.1910.14450.871
cg03591753FKBP50.09231.950.0250.479411.345.46E-17
cg08636224FKBP50.02250.960.9180.01450.091
cg00130530FKBP50.12254.897.78E-07−0.05780.231
cg20813374FKBP50.11944.651.80E-06−0.01450.011
cg01294490FKBP50.00640.0710.04330.721
cg07843056FKBP50.00210.0610.000701
cg16012111FKBP5−0.01650.51−0.010.041
cg10913456FKBP5−0.01691.341−0.01540.221
cg00140191FKBP5−0.00260.021−0.02090.281
cg00862770FKBP50.00230.041−0.000301
cg03546163FKBP50.20962.420.0050.58974.121.21E-05
cg14642437FKBP50.02110.1910.09540.851
cg17085721FKBP50.01640.3710.01240.041
cg19014730FKBP50.05680.5610.01980.011
cg07061368FKBP5−0.03370.331−0.15491.50.127
cg08586216FKBP50.01170.371−0.00480.011
cg16052510FKBP50.02470.1310.08860.371
cg14284211FKBP50.09882.290.0070.454310.431.65E-15
cg07633853FKBP50.04590.4310.37836.355.20E-09
cg10300814FKBP50.02821.030.7080.04260.511
cg06087101FKBP50.07590.5710.08020.141
cg02665568FKBP5−0.002101−0.00301
cg18726036FKBP50.00240.0210.00730.041

Appendix D

The smoking and alcohol consumption associated regression coefficient, percent variation explained and corrected p-value of DNA methylation probes at the NR3C1 and FKBP5 genes in the AlcMeth cohort.

SmokingAlcohol Consumption
CpGGeneRegression Coefficient% Variation ExplainedCorrected P-valueRegression Coefficient% Variation ExplainedCorrected P-value
cg12466613NR3C1−0.04062.1610.06320.241
cg07589972NR3C1−0.04123.871−0.328011.240.507
cg26720913NR3C1−0.00520.4010.01170.091
cg08818984NR3C10.00640.1710.03470.231
cg07528216NR3C1−0.00620.181−0.01370.041
cg27345592NR3C10.00320.0410.07560.991
cg13648501NR3C10.02802.1310.07710.741
cg24026230NR3C1−0.00250.131−0.03881.481
cg14558428NR3C1−0.00831.7110.06925.551
cg21702128NR3C10.00180.0510.10888.821
cg10847032NR3C1−0.02295.731−0.00360.011
cg16335926NR3C10.00381.101−0.02702.471
cg18849621NR3C10.00870.3910.07241.251
cg06968181NR3C10.01732.5210.00500.011
cg26464411NR3C10.01262.6010.08725.671
cg18068240NR3C10.00130.121−0.01841.031
cg15645634NR3C10.00641.5610.02841.411
cg15910486NR3C10.01915.971−0.03691.021
cg04111177NR3C10.00210.1010.08387.491
cg17860381NR3C1−0.00180.221−0.00930.271
cg18019515NR3C10.00040.0110.02793.661
cg11152298NR3C10.00871.6010.08677.321
cg00629244NR3C10.00400.5710.06907.891
cg18146873NR3C1−0.00040.0010.06963.501
cg20753294NR3C10.02182.561−0.07571.421
cg17617527NR3C10.00502.331−0.02302.201
cg06521673NR3C10.00340.3110.07546.891
cg06952416NR3C10.00290.0110.10020.761
cg27122725NR3C10.04103.6010.20754.231
cg18998365NR3C10.101911.000.558−0.04510.101
cg07733851NR3C10.01180.171−0.002601
cg08845721NR3C1−0.06818.391−0.382212.110.363
cg17342132NR3C1−0.145811.390.48−0.896519.690.018
cg06613263NR3C1−0.08766.981−0.23862.371
cg27107893NR3C10.06202.111−0.13260.501
cg25535999NR3C1−0.00130.011−0.05770.501
cg16586394NR3C1−0.02241.851−0.18615.841
cg18484679NR3C1−0.03673.251−0.07450.611
cg03857453NR3C10.220033.404.28E-050.883124.640.002
cg19457823NR3C1−0.10795.921−0.31832.361
cg23273257NR3C1−0.00450.121−0.08411.921
cg08915438FKBP50.200727.750.0010.877124.260.003
cg19226017FKBP50.163439.961.59E-060.549020.640.012
cg25114611FKBP50.161832.636.19E-050.758232.805.71E-05
cg17030679FKBP50.00380.2910.09929.251
cg07485685FKBP5−0.00140.101−0.00700.121
cg00610228FKBP50.00610.9010.07365.951
cg11845071FKBP5−0.00340.4410.01170.261
cg06937024FKBP50.00150.2610.03014.521
cg00052684FKBP50.01710.1011.007414.900.134
cg23416081FKBP50.353833.144.87E-051.609131.381.11E-04
cg15929276FKBP50.196515.460.0980.18880.651
cg03591753FKBP50.266728.594.00E-041.100822.300.006
cg08636224FKBP50.03538.1810.09612.781
cg00130530FKBP5−0.00480.0210.26082.241
cg20813374FKBP50.09907.7310.584312.330.334
cg01294490FKBP50.01180.4010.326413.850.184
cg07843056FKBP50.00210.1210.02420.741
cg16012111FKBP5−0.00620.5010.06332.351
cg10913456FKBP50.00351.5410.01431.241
cg00140191FKBP5−0.00400.251−0.09566.671
cg00862770FKBP50.00570.4710.07283.551
cg03546163FKBP50.180014.080.1690.916816.720.059
cg14642437FKBP50.06089.4810.356314.900.122
cg17085721FKBP5−0.02041.111−0.11191.531
cg19014730FKBP5−0.03480.8010.07320.161
cg07061368FKBP5−0.08696.221−0.42386.771
cg08586216FKBP5−0.00390.131−0.04580.821
cg16052510FKBP50.02810.5610.41225.551
cg14284211FKBP50.191321.580.0081.215839.911.63E-06
cg07633853FKBP50.05112.3910.44518.531
cg10300814FKBP50.044913.580.2050.14956.901
cg06087101FKBP50.07782.4810.000301
cg02665568FKBP5−0.01180.3710.003401
cg18726036FKBP50.01571.711−0.03480.381

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Authors' contributions All authors were involved in all aspects of the study. This includes design, analysis and interpretation of the data, drafting and revising the manuscript and has read and approved the final version of the manuscript.

Conflict of interest The use of DNA methylation to assess alcohol use status is covered by pending property claims. The use of DNA methylation to assess smoking status is covered by US patent 8,637,652 and other pending claims. Dr. Philibert is a potential royalty recipient on those intellectual right claims. Dr. Philibert is an officer and stockholder of Behavioral Diagnostics (www.bdmethylation.com).

References

  • Almén MS, Nilsson EK, Jacobsson JA, Kalnina I, Klovins J, Fredriksson R, Schiöth HB. Genome-wide analysis reveals DNA methylation markers that vary with both age and obesity. Gene. 2014;548:61–67. [PubMed]
  • Bell RL, Kimpel MW, McClintick JN, Strother WN, Carr LG, Liang T, Rodd ZA, Mayfield RD, Edenberg HJ, McBride WJ. Gene expression changes in the nucleus accumbens of alcohol-preferring rats following chronic ethanol consumption. Pharmacol. Biochem. Behav. 2009;94:131–147. [PMC free article] [PubMed]
  • Brauers E, Vester U, Zerres K, Eggermann T. Search for mutations in SLC1A5 (19q13) in cystinuria patients. J. Inherited Metab. Dis. 2005;28:1169–1171. [PubMed]
  • Breitling LP, Salzmann K, Rothenbacher D, Burwinkel B, Brenner H. Smoking, F2RL3 methylation, and prognosis in stable coronary heart disease. Eur. Heart J. 2012;33:2841–2848. [PubMed]
  • Burgess DJ, Fu SS, van Ryn M. Potential Unintended Consequences of Tobacco-Control Policies on Mothers Who Smoke: A Review of the Literature. Am. J. Prev. Med. 2009;37:S151–S158. [PubMed]
  • Caraballo RS, Giovino GA, Pechacek TF. Self-reported cigarette smoking vs. serum cotinine among U.S. adolescents. Nicotine Tobacco Res. 2004;6:19–25. [PubMed]
  • Caraballo RS, Giovino GA, Pechacek TF, Mowery PD. Factors associated with discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons aged 17 years or older: Third National Health and Nutrition Examination Survey, 1988–1994. Am. J. Epidemiol. 2001;153:807–814. [PubMed]
  • Corbett C, Armstrong MJ, Neuberger J. Tobacco Smoking and Solid Organ Transplantation. Transplantation. 2012;94:979–987. 910.1097/TP.1090b1013e318263ad318265b. [PubMed]
  • Daban C, Vieta E, Mackin P, Young AH. Hypothalamic-pituitary-adrenal Axis and Bipolar Disorder. Psychiatr. Clin. North Am. 2005;28:469–480. [PubMed]
  • de Leon J, Diaz FJ. A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr. Res. 2005;76:135–157. [PubMed]
  • Dogan MV, Shields B, Cutrona C, Gao L, Gibbons FX, Simons R, Monick M, Brody G, Tan K, Philibert R. The effect of smoking on DNA methylation of peripheral blood mononuclear cells from African American women. BMC Genomics. 2014;15:151. [PMC free article] [PubMed]
  • Erim Y, Böttcher M, Dahmen U, Beck O, Broelsch CE, Helander A. Urinary ethyl glucuronide testing detects alcohol consumption in alcoholic liver disease patients awaiting liver transplantation. Liver Transpl. 2007;13:757–761. [PubMed]
  • Florescu A, Ferrence R, Einarson T, Selby P, Soldin O, Koren G. Methods for Quantification of Exposure to Cigarette Smoking and Environmental Tobacco Smoke: Focus on Developmental Toxicology. Ther. Drug Monit. 2009;31:14–30. [PMC free article] [PubMed]
  • Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan J-B, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K. Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Mol. Cell. 2013;49:359–367. [PMC free article] [PubMed]
  • Huang M-C, Schwandt ML, Chester JA, Kirchhoff AM, Kao C-F, Liang T, Tapocik JD, Ramchandani VA, George DT, Hodgkinson CA, Goldman D, Heilig M. FKBP5 Moderates Alcohol Withdrawal Severity: Human Genetic Association and Functional Validation in Knockout Mice. Neuropsychopharmacology. 2014;39:2029–2038. [PMC free article] [PubMed]
  • Joubert BR, Håberg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK, Huang Z, Hoyo C, Midttun Ø, Cupul-Uicab LA, Ueland PM, Wu MC, Nystad W, Bell DA, Peddada SD, London SJ. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ. Health Perspect. 2012;120:1425–1431. [PMC free article] [PubMed]
  • Kandel DB, Schaffran C, Griesler PC, Hu M-C, Davies M, Benowitz N. Salivary Cotinine Concentration Versus Self-Reported Cigarette Smoking: Three Patterns of Inconsistency in Adolescence. Nicotine Tobacco Res. 2006;8:525–537. [PMC free article] [PubMed]
  • Kessler RC, Crum RM, Warner LA, Nelson CB, Schulenberg J, Anthony JC. Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Arch. Gen. Psychiatry. 1997;54:313–321. [PubMed]
  • Klengel T, Mehta D, Anacker C, Rex-Haffner M, Pruessner JC, Pariante CM, Pace TWW, Mercer KB, Mayberg HS, Bradley B, Nemeroff CB, Holsboer F, Heim CM, Ressler KJ, Rein T, Binder EB. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat. Neurosci. 2013;16:33–41. [PMC free article] [PubMed]
  • Monick MM, Beach SR, Plume J, Sears R, Gerrard M, Brody GH, Philibert RA. Coordinated changes in AHRR methylation in lymphoblasts and pulmonary macrophages from smokers. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2012;159B:141–151. [PMC free article] [PubMed]
  • Non AL, Binder AM, Barault L, Rancourt RC, Kubzansky LD, Michels KB. DNA methylation of stress-related genes and LINE-1 repetitive elements across the healthy human placenta. Placenta. 2012;33:183–187. [PMC free article] [PubMed]
  • Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3:97–106. [PubMed]
  • Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 2008;31:464–468. [PubMed]
  • Perroud N, Paoloni-Giacobino A, Prada P, Olie E, Salzmann A, Nicastro R, Guillaume S, Mouthon D, Stouder C, Dieben K, Huguelet P, Courtet P, Malafosse A. Increased methylation of glucocorticoid receptor gene (NR3C1) in adults with a history of childhood maltreatment: a link with the severity and type of trauma. Transl Psychiatry. 2011;1:e59. [PMC free article] [PubMed]
  • Philibert R, Hollenbeck N, Andersen E, Osborn T, Gerrard M, Gibbons R, Wang K. A Quantitative Epigenetic Approach for the Assessment of Cigarette Consumption. Front. Psychol. 2015;6:656. [PMC free article] [PubMed]
  • Philibert R, Penaluna B, White T, Shires S, Gunter TD, Liesveld J, Erwin C, Hollenbeck N, Osborn T. A pilot examination of the genome-wide DNA methylation signatures of subjects entering and exiting short-term alcohol dependence treatment programs. Epigenetics. 2014;9:1–7. [PMC free article] [PubMed]
  • Russell TV, Crawford MA, Woodby LL. Measurements for active cigarette smoke exposure in prevalence and cessation studies: Why simply asking pregnant women isn't enough. Nicotine Tobacco Res. 2004;6:S141–S151. [PubMed]
  • Swendsen J, Conway KP, Degenhardt L, Glantz M, Jin R, Merikangas KR, Sampson N, Kessler RC. Mental disorders as risk factors for substance use, abuse and dependence: results from the 10-year follow-up of the National Comorbidity Survey. Addiction. 2010;105:1117–1128. [PMC free article] [PubMed]
  • Tavakoli HR, Hull M, Okasinski LM. Review of current clinical biomarkers for the detection of alcohol dependence. Innov. Clin. Neurosci. 2011;8:26. [PubMed]
  • Team RC. R: A language and environment for statistical computing, R Foundation for Statistical Computing. Vienna, Austria: 2012.
  • Toperoff G, Aran D, Kark JD, Rosenberg M, Dubnikov T, Nissan B, Wainstein J, Friedlander Y, Levy-Lahad E, Glaser B, Hellman A. Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum. Mol. Genet. 2012;21:371–383. [PMC free article] [PubMed]
  • Webb DA, Boyd NR, Messina D, Windsor RA. The discrepancy between self-reported smoking status and urine continine levels among women enrolled in prenatal care at four publicly funded clinical sites. J. Public Health Manag. Pract. 2003;9:322–325. [PubMed]
  • Whitford JL, Widner SC, Mellick D, Elkins RL. Self-Report of Drinking Compared to Objective Markers of Alcohol Consumption. The American Journal of Drug and Alcohol Abuse. 2009;35:55–58. [PubMed]
  • Zeilinger S, Kühnel B, Klopp N, Baurecht H, Kleinschmidt A, Gieger C, Weidinger S, Lattka E, Adamski J, Peters A, Strauch K, Waldenberger M, Illig T. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One. 2013;8:e63812. [PMC free article] [PubMed]
  • Zhang Y, Yang R, Burwinkel B, Breitling LP, Holleczek B, Schöttker B, Brenner H. F2RL3 methylation in blood DNA is a strong predictor of mortality. Int. J. Epidemiol. 2014;43:1215–1225. [PMC free article] [PubMed]